Folder

📁 CSP007 S H A

Location

CyberSecPro

Contents

0 sub-folders
16 files | 130.65 MB

Description

This folder contains a collection of PowerPoint presentations and a document that explore anomaly detection and explainable AI in the healthcare sector, discussing cybersecurity challenges, statistics on data breaches, various machine learning algorithms, and practical training insights. Materials are available in English, Greek, and Italian, catering to diverse audiences interested in these critical topics. AI generated

• Anomaly Detection in the Health Sector English.pptx (1.11 MB)
  This PowerPoint presentation discusses the significance of anomaly detection and network security in the healthcare sector. It covers key topics such as the impact of cyberattacks on healthcare organizations, statistics on data breaches, and the importance of securing Internet of Medical Things devices to protect sensitive patient information. AI generated
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• Anomaly Detection in the Health Sector Greek.pptx (989.93 KB)
  This PowerPoint presentation focuses on anomaly detection in the health sector, highlighting the importance of network security due to the increasing risks of cyberattacks on healthcare organizations. It includes statistics on data breaches, notable cyber incidents, and discusses the Internet of Medical Things (IoMT) and the challenges associated with its cybersecurity. AI generated
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• Anomaly Detection in the Health Sector Italian.pptx (1.11 MB)
  This presentation discusses anomaly detection in the healthcare sector, emphasizing the importance of network security to protect patient data from cyberattacks. It includes statistics on data breaches, details of significant cyber incidents like the WannaCry ransomware attack, and explains the challenges of securing Internet of Medical Things (IoMT) devices. AI generated
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• Explainable AI for Healthcare-presentation English.pptx (21.31 MB)
  This presentation covers the intersection of explainable artificial intelligence (AI), adversarial machine learning, and cybersecurity in healthcare imaging. It highlights challenges in trust and security for medical image classification, introduces methods for explainability and robustness using deep learning techniques, and discusses various applications, including Alzheimer’s disease classification and diabetic retinopathy detection. AI generated
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• Explainable AI for Healthcare-presentation Greek.pptx (21.32 MB)
  This file is a presentation on cyber security in medical imaging, focusing on explainable artificial intelligence and adversarial machine learning. It covers definitions, applications, and challenges of using AI in healthcare imaging, discussing how adversarial attacks can jeopardize diagnosis reliability and the role of explainable AI in enhancing trust and security in medical decision-making processes. AI generated
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• Explainable AI for Healthcare-presentation Italian.pptx (21.30 MB)
  This presentation focuses on the application of Explainable AI and Adversarial Machine Learning in medical imaging. It discusses the importance of cybersecurity in healthcare, the roles of deep learning for image classification, challenges of adversarial attacks, and methods for enhancing reliability and safety in AI-assisted diagnostics. AI generated
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• Introduction to anomaly detection English.pptx (1.33 MB)
  This PowerPoint presentation provides a comprehensive introduction to anomaly detection, detailing its definition, types, and applications in various fields such as cybersecurity, finance, and healthcare. It categorizes anomalies, discusses detection methods and tools, and outlines common use cases, aiming to equip viewers with foundational knowledge of how to identify and respond to deviations from expected patterns. AI generated
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• Introduction to anomaly detection Greek.pptx (1.18 MB)
  This PowerPoint presentation provides an introduction to anomaly detection, including definitions, types of anomalies (such as point, contextual, and collective), and their significance across various industries like cybersecurity and finance. It discusses methods and tools used for detecting anomalies and emphasizes the need for further investigation into identified anomalies to determine their nature. AI generated
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• Introduction to anomaly detection Italian.pptx (1.32 MB)
  This presentation provides an introduction to anomaly detection, covering key concepts such as what constitutes an anomaly, the different categories of anomalies (point-based, contextual, and collective), and methodologies for detecting them. It discusses applications across various sectors, including cybersecurity, finance, and healthcare, and highlights algorithms and tools used for anomaly detection. The content is in Italian. AI generated
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• Links.docx (13.69 KB)
  This document contains a list of links to Google Colab notebooks. Each link provides access to specific resources or projects related to the research project. AI generated
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• Machine learning algorithms for anomaly detection English.pptx (1.19 MB)
  This presentation covers machine learning algorithms specifically for anomaly detection. It includes an introduction to machine learning, various types of algorithms such as One-Class SVM and Autoencoders, evaluation metrics, overfitting, and hyperparameter tuning, as well as practical implementation examples using the KDDCUP 1999 dataset. Ideal for those seeking to understand and apply machine learning methods to detect anomalies in data. AI generated
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• Machine learning algorithms for anomaly detection Greek.pptx (1.05 MB)
  This PowerPoint presentation provides an introduction to machine learning algorithms for anomaly detection, covering various techniques such as clustering, autoencoders, and one-class support vector machines. It details the steps involved in applying machine learning, including data collection, processing, and model evaluation, and addresses challenges like overfitting and hyperparameter tuning, with examples and references included. AI generated
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• Machine learning algorithms for anomaly detection Italian.pptx (1.19 MB)
  This PowerPoint presentation provides an overview of machine learning algorithms specifically for anomaly detection. It covers topics such as the stages of machine learning application, types of learning, evaluation metrics, and detailed methods for detecting anomalies, including Isolation Forest, OC-SVM, and autoencoders, along with their advantages and disadvantages. AI generated
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• Practical Insights in Anomaly Detection English.pptx (18.76 MB)
  This PowerPoint presentation focuses on anomaly detection, particularly within the context of cybersecurity in the health sector. It outlines training goals, logistics, key topics including machine learning methods, practical applications, and expected learning outcomes, all aimed at equipping participants with the skills needed to address cybersecurity threats effectively. AI generated
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• Practical Insights in Anomaly Detection Greek.pptx (18.74 MB)
  This presentation provides practical insights into anomaly detection in cybersecurity, particularly focused on the healthcare sector. It covers training objectives, learning outcomes, educational logistics, and core topics such as machine learning algorithms, real-world applications, and the unique challenges faced in ensuring cybersecurity within healthcare environments. Ideal for participants with a background in data analysis and programming. AI generated
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• Practical Insights in Anomaly Detection Italian.pptx (18.75 MB)
  This PowerPoint presentation provides practical insights into anomaly detection, focusing on its importance in cybersecurity, especially within the healthcare sector. It covers training logistics, learning outcomes, and methodologies, including machine learning techniques, with an emphasis on real-world applications and hands-on experience using Python. Suitable for individuals with a background in data analysis, statistics, or programming. AI generated
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Acronym

CyberSecPro

Title

Collaborative, Multi-modal and Agile Professional Cybersecurity Training Program for A Skilled Workforce In the European Digital Single Market and Industries

Description

CyberSecPro promotes a conceptual disruptive approach with regards to existing approaches to training solutions. The project contributes to the broader need for cybersecurity marketing skills and technical/practical capabilities.
CyberSecPro aims to upskill and develop a workforce capable of overcoming the increasing security challenges that will burden innovation and excellence. This overarching goal can be achieved with the methodology adopted in the CyberSecPro project.
Further, the project utilises the processes developed within many EU Cybersecurity pilot projects and adppts relevant EU innovation and development work:

CyberSecPro will drive the HEIs to further enhance their cooperation with the private sector, in order to become the main suppliers of the necessary market-oriented cybersecurity skills and working-life practices required in the digital transformation, via providing hands-on trainings.

Project Reference Code/ID

N/A

Level

N/A

Copyright license

N/A

Materials

747 files | 8.38 GB

Last updated

05/2026

Download Statistics

964 file downloads | 10.02 GB